Abstract
User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for dialectal content, where in addition to spelling and lexical differences, dialectal text is characterized with morpho-syntactic and phonetic variations. These issues increase sparsity in NLP models and reduce accuracy. We present a neural morphological tagging and disambiguation model for Egyptian Arabic, with various extensions to handle noisy and inconsistent content. Our models achieve about 5% relative error reduction (1.1% absolute improvement) for full morphological analysis, and around 22% relative error reduction (1.8% absolute improvement) for part-of-speech tagging, over a state-of-the-art baseline.- Anthology ID:
- N18-1087
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 953–964
- Language:
- URL:
- https://aclanthology.org/N18-1087
- DOI:
- 10.18653/v1/N18-1087
- Cite (ACL):
- Nasser Zalmout, Alexander Erdmann, and Nizar Habash. 2018. Noise-Robust Morphological Disambiguation for Dialectal Arabic. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 953–964, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Noise-Robust Morphological Disambiguation for Dialectal Arabic (Zalmout et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/N18-1087.pdf